The transportation industry serves as an integral part of modern society's infrastructure. Every day millions of people travel on major highways across the world. As population numbers increase and nations become more industrialized and more prosperous, the use of this existing transportation infrastructure may be stretched beyond its limits. This pressure on the existing transportation infrastructure brings with it higher incidence of accidents, increased environmental pollution and traffic congestion. An alternative to building new roads is to make more efficient use of existing roads through alternate modes of transportation. Such is the approach taken by the Transportation Demand Management (TDM) industry. This alternate approach has the added advantage of alleviating problems associated with increased numbers of vehicles utilizing the roadways, such as increased costs to provide parking, increased traffic accidents and increased environmental pollution. The approach taken by the TDM additionally provides for transportation solutions for non-drivers.
In order to provide effective solutions to increased transportation demands, the TDM industry requires an in-depth understanding of the transportation patterns of the local area. In the past this has meant relying on paper-based surveys to analyze traffic flow, trip planning, and the use of public transportation. However, the paper-based surveys known in the prior art for transportation analysis have several major intrinsic flaws. First, since the amount of effort required to complete these surveys is significant (on the order of 30 minutes per day) recruiting participants to participate in the studies has been proved difficult. Second, the accuracy of the data is questionable due to user error, apathy, and intentional or unintentional omissions. Third, once the surveys are collected, they must he manually processed which in turn requires a significant amount of tine and effort for the project to be successfully completed. Additionally, it is often the case that the data collected from these individual surveys must be cross-referenced with the data collected from other individuals of the same household to be able to better understand travel mode selection behavior. This cross-referencing requires converting the manually collected data to electronic format so that more powerful and sophisticated analysis tools can be used to perform the comparative analysis.
It is known in the art to employ Global Positioning Systems (GPS) to identify the location of an individual. Typically, the GPS devices are attached to a single vehicle and are therefore not “user” based but “vehicle” based. These systems miss recording any trips that are taken by bike, walking, or transit. When collecting data, it is preferable to record an individual's complete travel behavior rather than only trips taken in a single vehicle.
Accordingly, what is needed in the art is a system and method for tracking the travel patterns of individuals that does not require the use of time-consuming paper or telephone based surveys and analyses.